التفاصيل البيبلوغرافية
| العنوان: |
A Contemporary Review on Drought Modeling Using Machine Learning Approaches. |
| المؤلفون: |
Sundararajan, Karpagam, Garg, Lalit, Srinivasan, Kathiravan, Bashir, Ali Kashif, Kaliappan, Jayakumar, Ganapathy, Ganapathy Pattukandan, Selvaraj, Senthil Kumaran, Meena, T. |
| المصدر: |
CMES-Computer Modeling in Engineering & Sciences; 2021, Vol. 128 Issue 2, p447-487, 41p |
| مصطلحات موضوعية: |
DROUGHT management, MACHINE learning, DEEP learning, COMPUTATIONAL intelligence, WEATHER forecasting, MOVING average process, DROUGHTS, NATURAL disasters |
| مصطلحات جغرافية: |
INDIA |
| مستخلص: |
Drought is the least understood natural disaster due to the complex relationship of multiple contributory factors. Its beginning and end are hard to gauge, and they can last for months or even for years. India has faced many droughts in the last few decades. Predicting future droughts is vital for framing drought management plans to sustain natural resources. The data-driven modelling for forecasting the metrological time series prediction is becoming more powerful and flexible with computational intelligence techniques. Machine learning (ML) techniques have demonstrated success in the drought prediction process and are becoming popular to predict the weather, especially the minimumtemperature using backpropagation algorithms. The favouriteML techniques for weather forecasting include singular vector machines (SVM), support vector regression, random forest, decision tree, logistic regression, Naive Bayes, linear regression, gradient boosting tree, k-nearest neighbours (KNN), the adaptive neuro-fuzzy inference system, the feed-forward neural networks, Markovian chain, Bayesian network, hidden Markov models, and autoregressive moving averages, evolutionary algorithms, deep learning and many more. This paper presents a recent review of the literature using ML in drought prediction, the drought indices, dataset, and performance metrics. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Complementary Index |
